#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Road Traffic Injury Dataset."""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os

SCENARIOS = ['trauma_centre', 'district_hospital', 'rural_health_centre']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'rti_{sc}.csv')
        if os.path.exists(path):
            dfs[sc] = pd.read_csv(path)
    return dfs


def make_report(dfs, output='validation_report.png'):
    fig, axes = plt.subplots(4, 2, figsize=(16, 22))
    fig.suptitle('Road Traffic Injury & Trauma — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)
    df = dfs.get('district_hospital', list(dfs.values())[0])
    colors = ['#2ecc71', '#f39c12', '#e74c3c']

    # Panel 1: Mortality across scenarios
    ax = axes[0, 0]
    x = np.arange(len(SCENARIOS))
    mort = [(dfs[sc]['outcome'] == 'died').mean() * 100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, mort, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Trauma Centre', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(mort):
        ax.text(i, v + 0.3, f'{v:.1f}%', ha='center', fontsize=10)
    ax.set_ylabel('Mortality (%)')
    ax.set_title('RTI Mortality (Africa: 26.6/100K)')

    # Panel 2: Road user type
    ax = axes[0, 1]
    users = df['road_user_type'].value_counts()
    u_colors = ['#e74c3c', '#3498db', '#f39c12', '#2ecc71', '#9b59b6', '#e67e22']
    ax.pie(users.values,
           labels=[u.replace('_', ' ').title() for u in users.index],
           autopct='%1.1f%%', colors=u_colors[:len(users)],
           startangle=90, textprops={'fontsize': 8})
    ax.set_title('Road User Type (WHO: Pedestrians >50% Africa)')

    # Panel 3: GCS vs mortality
    ax = axes[1, 0]
    gcs_cats = ['severe', 'moderate', 'mild']
    gcs_mort = []
    for g in gcs_cats:
        sub = df[df['gcs_category'] == g]
        gcs_mort.append((sub['outcome'] == 'died').mean() * 100 if len(sub) > 0 else 0)
    g_colors = ['#e74c3c', '#f39c12', '#2ecc71']
    ax.bar(range(3), gcs_mort, color=g_colors, alpha=0.8)
    ax.set_xticks(range(3))
    ax.set_xticklabels(['Severe (3-8)', 'Moderate (9-12)', 'Mild (13-15)'])
    for i, v in enumerate(gcs_mort):
        ax.text(i, v + 0.3, f'{v:.0f}%', ha='center', fontsize=9)
    ax.set_ylabel('Mortality (%)')
    ax.set_title('Mortality by GCS Category')

    # Panel 4: Body region
    ax = axes[1, 1]
    regions = df['primary_body_region'].value_counts()
    ax.barh(range(len(regions)), regions.values, color='#3498db', alpha=0.7)
    ax.set_yticks(range(len(regions)))
    ax.set_yticklabels([r.replace('_', ' ').title()[:15] for r in regions.index], fontsize=7)
    ax.set_xlabel('Count')
    ax.set_title('Primary Body Region (Head 28%, Extremity 32%)')

    # Panel 5: Ambulance & golden hour
    ax = axes[2, 0]
    amb = [(dfs[sc]['transport_mode'] == 'ambulance').mean() * 100 for sc in SCENARIOS if sc in dfs]
    gh = [dfs[sc]['within_golden_hour'].mean() * 100 for sc in SCENARIOS if sc in dfs]
    w = 0.3
    ax.bar(x - w/2, amb, w, label='Ambulance', color='#3498db', alpha=0.8)
    ax.bar(x + w/2, gh, w, label='Golden Hour', color='#f39c12', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Trauma', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Prehospital: Ambulance & Golden Hour')
    ax.legend(fontsize=8)

    # Panel 6: ISS distribution
    ax = axes[2, 1]
    for sc in SCENARIOS:
        if sc in dfs:
            ax.hist(dfs[sc]['iss'].clip(1, 50), bins=20, alpha=0.5,
                    label=sc.replace('_', ' ').title()[:12], edgecolor='white')
    ax.axvline(x=16, color='red', linestyle='--', alpha=0.7, label='Severe (ISS≥16)')
    ax.set_xlabel('ISS')
    ax.set_title('Injury Severity Score Distribution')
    ax.legend(fontsize=7)

    # Panel 7: Age-sex distribution
    ax = axes[3, 0]
    males = df[df['sex'] == 'M']['age_years']
    females = df[df['sex'] == 'F']['age_years']
    ax.hist(males, bins=15, alpha=0.5, color='#3498db', label='Male', edgecolor='white')
    ax.hist(females, bins=15, alpha=0.5, color='#e74c3c', label='Female', edgecolor='white')
    ax.set_xlabel('Age (years)')
    ax.set_title('Age-Sex Distribution (Males 75%, peak 15-44y)')
    ax.legend(fontsize=8)

    # Panel 8: Disability at discharge
    ax = axes[3, 1]
    surv = df[df['outcome'] == 'survived']
    if len(surv) > 0:
        dis = surv['disability_at_discharge'].value_counts()
        d_colors = ['#2ecc71', '#f39c12', '#e74c3c', '#9b59b6', '#3498db']
        ax.pie(dis.values,
               labels=[d.replace('_', ' ').title() for d in dis.index],
               autopct='%1.1f%%', colors=d_colors[:len(dis)],
               startangle=90, textprops={'fontsize': 8})
    ax.set_title('Disability at Discharge (Survivors)')

    plt.tight_layout(rect=[0, 0, 1, 0.97])
    plt.savefig(output, dpi=150, bbox_inches='tight')
    print(f'Saved validation report to {output}')
    plt.close()


if __name__ == '__main__':
    dfs = load_scenarios()
    if dfs:
        make_report(dfs)